{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## scikit-learn实例"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.model_selection import train_test_split\n",
    "from collections import Counter\n",
    "import math\n",
    "from math import log\n",
    "import pprint\n",
    "\n",
    "from sklearn.tree import DecisionTreeClassifier,plot_tree\n",
    "#from sklearn.tree import export_graphviz\n",
    "#import graphviz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data\n",
    "def create_data():\n",
    "    iris = load_iris()\n",
    "    df = pd.DataFrame(iris.data, columns=iris.feature_names)\n",
    "    df['label'] = iris.target\n",
    "    df.columns = [\n",
    "        'sepal length', 'sepal width', 'petal length', 'petal width', 'label'\n",
    "    ]\n",
    "    data = np.array(df.iloc[:100, [0, 1, -1]])\n",
    "    # print(data)\n",
    "    return data[:, :2], data[:, -1]\n",
    "\n",
    "\n",
    "X, y = create_data()\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
       "                       max_features=None, max_leaf_nodes=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, presort=False,\n",
       "                       random_state=None, splitter='best')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#criterion：\"gini\"  \"entropy\"\n",
    "clf = DecisionTreeClassifier()\n",
    "clf.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9666666666666667"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "plot_tree(clf, filled=True,feature_names=['length','width'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ID3 计算信息增益"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 熵\n",
    "def calc_ent(datasets):\n",
    "    #样本个数\n",
    "    data_length = len(datasets)\n",
    "    label_count = {}\n",
    "    #统计是标签个数 否标签个数\n",
    "    for i in range(data_length):\n",
    "        label = datasets[i][-1]\n",
    "        if label not in label_count:\n",
    "            label_count[label] = 0\n",
    "        label_count[label] += 1\n",
    "    #计算标签序列的熵\n",
    "    ent = -sum([(p / data_length) * log(p / data_length, 2)\n",
    "                for p in label_count.values()])\n",
    "    return ent\n",
    "# def entropy(y):\n",
    "#     \"\"\"\n",
    "#     Entropy of a label sequence\n",
    "#     \"\"\"\n",
    "#     hist = np.bincount(y)\n",
    "#     ps = hist / np.sum(hist)\n",
    "#     return -np.sum([p * np.log2(p) for p in ps if p > 0])\n",
    "\n",
    "\n",
    "# 经验条件熵\n",
    "def cond_ent(datasets, axis=0):\n",
    "    data_length = len(datasets)\n",
    "    feature_sets = {}\n",
    "    #按某列特征，形成feature_sets= {'青年': [['青年', '否', '否', '一般', '否'],...] '中年': [['中年', '否', '否', '一般', '否'],...]..}\n",
    "    for i in range(data_length):\n",
    "        feature = datasets[i][axis]\n",
    "        if feature not in feature_sets:\n",
    "            feature_sets[feature] = []\n",
    "        feature_sets[feature].append(datasets[i])\n",
    "    #循环特征列 计算\n",
    "    cond_ent = sum(\n",
    "        [(len(p) / data_length) * calc_ent(p) for p in feature_sets.values()])\n",
    "    return cond_ent\n",
    "\n",
    "\n",
    "# 信息增益\n",
    "def info_gain(ent, cond_ent):\n",
    "    return ent - cond_ent\n",
    "\n",
    "\n",
    "def info_gain_train(datasets):\n",
    "    #4列特征\n",
    "    count = len(datasets[0]) - 1\n",
    "    #计算标签序列的熵\n",
    "    ent = calc_ent(datasets)\n",
    "#     ent = entropy(datasets)\n",
    "    best_feature = []\n",
    "    for c in range(count):\n",
    "        c_info_gain = info_gain(ent, cond_ent(datasets, axis=c))\n",
    "        best_feature.append((c, c_info_gain))\n",
    "        print('特征({}) - info_gain - {:.3f}'.format(labels[c], c_info_gain))\n",
    "    # 比较大小\n",
    "    best_ = max(best_feature, key=lambda x: x[-1])\n",
    "    return '特征({})的信息增益最大，选择为根节点特征'.format(labels[best_[0]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_data():\n",
    "    datasets = [['青年', '否', '否', '一般', '否'],\n",
    "               ['青年', '否', '否', '好', '否'],\n",
    "               ['青年', '是', '否', '好', '是'],\n",
    "               ['青年', '是', '是', '一般', '是'],\n",
    "               ['青年', '否', '否', '一般', '否'],\n",
    "               ['中年', '否', '否', '一般', '否'],\n",
    "               ['中年', '否', '否', '好', '否'],\n",
    "               ['中年', '是', '是', '好', '是'],\n",
    "               ['中年', '否', '是', '非常好', '是'],\n",
    "               ['中年', '否', '是', '非常好', '是'],\n",
    "               ['老年', '否', '是', '非常好', '是'],\n",
    "               ['老年', '否', '是', '好', '是'],\n",
    "               ['老年', '是', '否', '好', '是'],\n",
    "               ['老年', '是', '否', '非常好', '是'],\n",
    "               ['老年', '否', '否', '一般', '否'],\n",
    "               ]\n",
    "    labels = [u'年龄', u'有工作', u'有自己的房子', u'信贷情况', u'类别']\n",
    "    # 返回数据集和每个维度的名称\n",
    "    return datasets, labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "特征(年龄) - info_gain - 0.083\n",
      "特征(有工作) - info_gain - 0.324\n",
      "特征(有自己的房子) - info_gain - 0.420\n",
      "特征(信贷情况) - info_gain - 0.363\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'特征(有自己的房子)的信息增益最大，选择为根节点特征'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datasets, labels = create_data()\n",
    "\n",
    "info_gain_train(np.array(datasets))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 利用ID3算法生成决策树，例5.3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'label:': None, 'feature': 2, 'tree': {'否': {'label:': None, 'feature': 1, 'tree': {'否': {'label:': '否', 'feature': None, 'tree': {}}, '是': {'label:': '是', 'feature': None, 'tree': {}}}}, '是': {'label:': '是', 'feature': None, 'tree': {}}}}\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'否'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 定义节点类 二叉树\n",
    "class Node:\n",
    "    def __init__(self, root=True, label=None, feature_name=None, feature=None):\n",
    "        self.root = root\n",
    "        self.label = label\n",
    "        self.feature_name = feature_name\n",
    "        self.feature = feature\n",
    "        self.tree = {}\n",
    "        self.result = {\n",
    "            'label:': self.label,\n",
    "            'feature': self.feature,\n",
    "            'tree': self.tree\n",
    "        }\n",
    "\n",
    "    def __repr__(self):\n",
    "        return '{}'.format(self.result)\n",
    "\n",
    "    def add_node(self, val, node):\n",
    "        self.tree[val] = node\n",
    "\n",
    "    def predict(self, features):\n",
    "        if self.root is True:\n",
    "            return self.label\n",
    "        return self.tree[features[self.feature]].predict(features)\n",
    "\n",
    "class DTree:\n",
    "    def __init__(self, epsilon=0.1):\n",
    "        self.epsilon = epsilon\n",
    "        self._tree = {}\n",
    "\n",
    "    # 熵\n",
    "    @staticmethod\n",
    "    def calc_ent(datasets):\n",
    "        data_length = len(datasets)\n",
    "        label_count = {}\n",
    "        for i in range(data_length):\n",
    "            label = datasets[i][-1]\n",
    "            if label not in label_count:\n",
    "                label_count[label] = 0\n",
    "            label_count[label] += 1\n",
    "        ent = -sum([(p / data_length) * log(p / data_length, 2)\n",
    "                    for p in label_count.values()])\n",
    "        return ent\n",
    "\n",
    "    # 经验条件熵\n",
    "    def cond_ent(self, datasets, axis=0):\n",
    "        data_length = len(datasets)\n",
    "        feature_sets = {}\n",
    "        for i in range(data_length):\n",
    "            feature = datasets[i][axis]\n",
    "            if feature not in feature_sets:\n",
    "                feature_sets[feature] = []\n",
    "            feature_sets[feature].append(datasets[i])\n",
    "        cond_ent = sum([(len(p) / data_length) * self.calc_ent(p)\n",
    "                        for p in feature_sets.values()])\n",
    "        return cond_ent\n",
    "\n",
    "    # 信息增益\n",
    "    @staticmethod\n",
    "    def info_gain(ent, cond_ent):\n",
    "        return ent - cond_ent\n",
    "\n",
    "    def info_gain_train(self, datasets):\n",
    "        count = len(datasets[0]) - 1\n",
    "        ent = self.calc_ent(datasets)\n",
    "        best_feature = []\n",
    "        for c in range(count):\n",
    "            c_info_gain = self.info_gain(ent, self.cond_ent(datasets, axis=c))\n",
    "            best_feature.append((c, c_info_gain))\n",
    "        # 比较大小\n",
    "        best_ = max(best_feature, key=lambda x: x[-1])\n",
    "        return best_\n",
    "\n",
    "    def train(self, train_data):\n",
    "        \"\"\"\n",
    "        input:数据集D(DataFrame格式)，特征集A，阈值eta\n",
    "        output:决策树T\n",
    "        \"\"\"\n",
    "        _, y_train, features = train_data.iloc[:, :\n",
    "                                               -1], train_data.iloc[:,\n",
    "                                                                    -1], train_data.columns[:\n",
    "                                                                                            -1]\n",
    "        # 1,若D中实例属于同一类Ck，则T为单节点树，并将类Ck作为结点的类标记，返回T\n",
    "        if len(y_train.value_counts()) == 1:\n",
    "            return Node(root=True, label=y_train.iloc[0])\n",
    "\n",
    "        # 2, 若A为空，则T为单节点树，将D中实例树最大的类Ck作为该节点的类标记，返回T\n",
    "        if len(features) == 0:\n",
    "            return Node(\n",
    "                root=True,\n",
    "                label=y_train.value_counts().sort_values(\n",
    "                    ascending=False).index[0])\n",
    "\n",
    "        # 3,计算最大信息增益 同5.1,Ag为信息增益最大的特征\n",
    "        max_feature, max_info_gain = self.info_gain_train(np.array(train_data))\n",
    "        max_feature_name = features[max_feature]\n",
    "\n",
    "        # 4,Ag的信息增益小于阈值eta,则置T为单节点树，并将D中是实例数最大的类Ck作为该节点的类标记，返回T\n",
    "        if max_info_gain < self.epsilon:\n",
    "            return Node(\n",
    "                root=True,\n",
    "                label=y_train.value_counts().sort_values(\n",
    "                    ascending=False).index[0])\n",
    "\n",
    "        # 5,构建Ag子集\n",
    "        node_tree = Node(\n",
    "            root=False, feature_name=max_feature_name, feature=max_feature)\n",
    "\n",
    "        feature_list = train_data[max_feature_name].value_counts().index\n",
    "        for f in feature_list:\n",
    "            sub_train_df = train_data.loc[train_data[max_feature_name] ==\n",
    "                                          f].drop([max_feature_name], axis=1)\n",
    "\n",
    "            # 6, 递归生成树\n",
    "            sub_tree = self.train(sub_train_df)\n",
    "            node_tree.add_node(f, sub_tree)\n",
    "\n",
    "        # pprint.pprint(node_tree.tree)\n",
    "        return node_tree\n",
    "\n",
    "    def fit(self, train_data):\n",
    "        self._tree = self.train(train_data)\n",
    "        return self._tree\n",
    "\n",
    "    def predict(self, X_test):\n",
    "        return self._tree.predict(X_test)\n",
    "    \n",
    "datasets, labels = create_data()\n",
    "data_df = pd.DataFrame(datasets, columns=labels)\n",
    "dt = DTree()\n",
    "tree = dt.fit(data_df)\n",
    "\n",
    "print(tree)\n",
    "\n",
    "dt.predict(['老年', '否', '否', '一般'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## sickit-learn 解决方案 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn import preprocessing\n",
    "\n",
    "datasets, labels = create_data()\n",
    "data_df = pd.DataFrame(datasets, columns=labels)\n",
    "X_train = data_df[[u'年龄', u'有工作', u'有自己的房子', u'信贷情况']]\n",
    "y_train = data_df[['类别']]\n",
    "\n",
    "# 数据预处理\n",
    "le_x = preprocessing.LabelEncoder()\n",
    "le_x.fit(np.unique(X_train))\n",
    "X_train = X_train.apply(le_x.transform)\n",
    "le_y = preprocessing.LabelEncoder()\n",
    "le_y.fit(np.unique(y_train))\n",
    "y_train = y_train.apply(le_y.transform)\n",
    "# 调用sklearn.DT建立训练模型\n",
    "model_tree = DecisionTreeClassifier()\n",
    "model_tree.fit(X_train, y_train)\n",
    "\n",
    "plt.figure(figsize=(6,6))\n",
    "plot_tree(model_tree, filled=True,feature_names=[u'year',u'work',u'child',u'cre'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'feature': 0,\n",
       " 'value': 5,\n",
       " 'left': {'feature': 0, 'value': 3, 'left': 4.72, 'right': 5.57},\n",
       " 'right': {'feature': 0,\n",
       "  'value': 7,\n",
       "  'left': {'feature': 0, 'value': 6, 'left': 7.05, 'right': 7.9},\n",
       "  'right': {'feature': 0, 'value': 8, 'left': 8.23, 'right': 8.85}}}"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class LeastSqRTree:\n",
    "    def __init__(self, train_X, y, epsilon):\n",
    "        # 训练集特征值\n",
    "        self.x = train_X\n",
    "        # 类别\n",
    "        self.y = y\n",
    "        # 特征总数\n",
    "        self.feature_count = train_X.shape[1]\n",
    "        # 损失阈值\n",
    "        self.epsilon = epsilon\n",
    "        # 回归树\n",
    "        self.tree = None\n",
    "\n",
    "    def _fit(self, x, y, feature_count, epsilon):\n",
    "        # 选择最优切分点变量j与切分点s\n",
    "        (j, s, minval, c1, c2) = self._divide(x, y, feature_count)\n",
    "        # 初始化树\n",
    "        tree = {\"feature\": j, \"value\": x[s, j], \"left\": None, \"right\": None}\n",
    "        if minval < self.epsilon or len(y[np.where(x[:, j] <= x[s, j])]) <= 1:\n",
    "            tree[\"left\"] = c1\n",
    "        else:\n",
    "            tree[\"left\"] = self._fit(x[np.where(x[:, j] <= x[s, j])],\n",
    "                                     y[np.where(x[:, j] <= x[s, j])],\n",
    "                                     self.feature_count, self.epsilon)\n",
    "        if minval < self.epsilon or len(y[np.where(x[:, j] > s)]) <= 1:\n",
    "            tree[\"right\"] = c2\n",
    "        else:\n",
    "            tree[\"right\"] = self._fit(x[np.where(x[:, j] > x[s, j])],\n",
    "                                      y[np.where(x[:, j] > x[s, j])],\n",
    "                                      self.feature_count, self.epsilon)\n",
    "        return tree\n",
    "\n",
    "    def fit(self):\n",
    "        self.tree = self._fit(self.x, self.y, self.feature_count, self.epsilon)\n",
    "\n",
    "    @staticmethod\n",
    "    def _divide(x, y, feature_count):\n",
    "        # 初始化损失误差\n",
    "        cost = np.zeros((feature_count, len(x)))\n",
    "        # 公式5.21     y1[4.50]    y2[4.75, 4.91, 5.34, 5.80, 7.05, 7.90, 8.23, 8.70, 9.00] 逐个计算方差\n",
    "        for i in range(feature_count):\n",
    "            for k in range(len(x)):\n",
    "                # k行i列的特征值\n",
    "                value = x[k, i]\n",
    "                y1 = y[np.where(x[:, i] <= value)]\n",
    "                c1 = np.mean(y1)\n",
    "                y2 = y[np.where(x[:, i] > value)]\n",
    "                c2 = np.mean(y2)\n",
    "                y1[:] = y1[:] - c1\n",
    "                y2[:] = y2[:] - c2\n",
    "                cost[i, k] = np.sum(y1 * y1) + np.sum(y2 * y2)\n",
    "        # 选取最优损失误差点\n",
    "        cost_index = np.where(cost == np.min(cost))\n",
    "        # 选取第几个特征值\n",
    "        j = cost_index[0][0]\n",
    "        # 选取特征值的切分点\n",
    "        s = cost_index[1][0]\n",
    "        # 求两个区域的均值c1,c2\n",
    "        c1 = np.mean(y[np.where(x[:, j] <= x[s, j])])\n",
    "        c2 = np.mean(y[np.where(x[:, j] > x[s, j])])\n",
    "        return j, s, cost[cost_index], c1, c2\n",
    "\n",
    "#如果用两个维度的数据，出现两列特征最小方差相等时，会有一个bug，维度问题，不能通过\n",
    "#train_X = np.array([[1, 2, 3, 4, 5, 6, 7, 8, 9, 10],[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]]).T\n",
    "train_X = np.array([[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]]).T\n",
    "y = np.array([4.50, 4.75, 4.91, 5.34, 5.80, 7.05, 7.90, 8.23, 8.70, 9.00])\n",
    "\n",
    "model_tree = LeastSqRTree(train_X, y, .2)\n",
    "\n",
    "model_tree.fit()\n",
    "model_tree.tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "         \n",
    "          5       \n",
    "      3        7\n",
    "            6    8"
   ]
  }
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